Below you will find several empty R code scripts and answer prompts. Your task is to fill in the required code snippets and answer the corresponding questions.
Today, we start by looking at a collection of breakfast cereals:
With variables:
Produce a histogram of the sugar variable.
Now, compute the standard deviation of the variable sugar:
## [1] 4.378656
What are the units of this measurement?
Answer: grams (of sugar per serving)
Now, compute the deciles of the variable score:
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 18.0 28.0 31.0 34.5 37.0 40.0 42.0 48.0 53.0 58.0 84.0
What is the value of the 30th percentile. Describe what this means in words:
Answer: The value of the 30th percentile is about 34.5. This means that 30% of the cereals have below about 34.5 grams of sugar and 70% of the cereals have above about 34.5 grams of sugar.
Produce a boxplot of score and brand.
Which brand seems to have the healthiest cereals?
Answer: Nabisco seems to generally have the healthiest cereals.
Produce a boxplot of score and shelf.
Produce a boxplot of sugar and shelf.
If I want a healthy but reasonably sweet cereal which shelf would be the best to look on?
Answer: The top shelf would be best to look on. The top and bottom shelves generally have healthier options than the middle shelf. Considering that you want a reasonably sweet cereal and the bottom shelf generally has less sugar than the top shelf (as seen in the second boxplot), I conclude that the top shelf is the optimal shelf to search on.
Next, we will take another look at a dataset of tea reviews that I used in a previous lecture:
With variables: - name: the full name of the tea - type: the type of tea. One of: - black - chai - decaf - flavors - green - herbal - masters - matcha - oolong - pu_erh - rooibos - white - score: user rated score; from 0 to 100 - price: estimated price of one cup of tea - num_reviews: total number of online reviews
Draw a scatterplot with num_reviews (x-axis) against score (y-axis) and add a regression line (recall: geom_smooth(method="lm")).
Does the score tend to increase, decrease, or remain the same as the number of reviews increases?
Answer: Scores tend to increase as the number of reviews increases.
Calculate the ventiles of the variable price.
## 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
## 8.00 10.00 10.00 10.00 10.00 10.00 12.00 12.00 12.00 12.00
## 50% 55% 60% 65% 70% 75% 80% 85% 90% 95%
## 13.00 15.00 15.00 17.00 19.00 20.00 30.00 35.35 49.30 86.75
## 100%
## 196.00
What is the 80th percentile? Describe it in words, include the units of the problem in your answer.
Answer: The 80th percentile is 30 dollars. This means that 80% of the teas have an estimated price of less than 30 dollars, while 20% of the teas have an estimated price of greater than 30 dollars.
Plot the number of reviews (x-axis) against the score variable. Color the points according to price binned into 5 buckets.
What tends to be true about the number of reviews for the most expensive 20% of teas?
Answer: They tend not to have many reviews.
Create a dataset named white that consists of only white teas.
Calculate the standard deviation of the price for white teas and the standard deviation of the price for all of the teas.
## [1] 13.59444
Is the variation of the white tea prices smaller, larger, or about the same as the entire dataset?
Answer: The standard deviation of the entire dataset was previously calculated to be about 4.378656, which means that the standard devation of the price of only the white teas is much larger than that of the entire dataset.
Summarize the dataset by the type of tea and save the results as a variable named tea_type.
## # A tibble: 12 x 14
## type score_mean price_mean num_reviews_mean score_median
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 black 93.66667 23.37037 995.1852 94.0
## 2 chai 93.33333 12.00000 1069.4444 93.0
## 3 decaf 93.15385 15.00000 302.6154 94.0
## 4 flavors 92.00000 10.00000 890.8421 92.0
## 5 green 93.00000 17.94737 668.2632 93.0
## 6 herbal 93.19231 11.61538 916.1923 93.0
## 7 masters 94.60000 123.66667 114.5333 95.0
## 8 matcha 91.00000 60.00000 107.6667 92.0
## 9 oolong 93.50000 28.93750 635.8125 94.0
## 10 pu_erh 91.57143 20.57143 473.4286 92.0
## 11 rooibos 92.30769 11.73077 508.6538 92.5
## 12 white 92.70588 26.94118 632.8235 93.0
## # ... with 9 more variables: price_median <dbl>, num_reviews_median <dbl>,
## # score_sd <dbl>, price_sd <dbl>, num_reviews_sd <dbl>, score_sum <int>,
## # price_sum <int>, num_reviews_sum <int>, n <int>
Plot the average price (x-axis) against the average score (y-axis) of each type of tea. Make the size of the points proportional to the number of teas in each category and label the points with geom_text_repel and the tea type.
Describe an interesting pattern or set of outliers that you found in the previous plot. This does not need to take more than 1-2 sentences.
Answer: One interesting outlier is the matcha type teas, of which there are not very many in number. The price of the matcha teas are generally much higher than the other types, but also have the lowest average score.